This study investigates the theoretical background of the interpolation methods that regardsthe topographical effect on the climate data, such as Co-kriging, Artificial Neural Network and MKPRISM(Modified Korean Parameter-elevation Regressions on Independent Slopes Model). Prior toapplying the MK-PRISM to the interpolation of wind speed, this study has improved the model tobe closer to the fundamental concept of the PRISM and verified it‘s validity. Since each method hasindividual advantages and disadvantages, there will be a need for comparative studies in order to select aninterpolation method that is suitable for the topography of Korea. This study has added a weighted valuethat considers the existence of clusters at the known point, and has supplemented the digital elevationmodels and aspects distribution of multiple scales for application. In addition, this study has allowedthe consideration of sharp changes between the known point and unknown point when calculating thetopographic facet weighting. The supplement model was verified through the interpolation of rainfall inJeju Island. The coefficient of determination and KGE(Kling and Gupta Efficiency) of the model displayedthe results of 0.86 and 0.87, respectively for August 2010 monthly precipitation in Jeju Island, and themodel was accordingly verified. This study is able to provide the necessary information to the researcherswho wish to interpolate the observation data of wind speed. Furthermore, the supplement MK-PRISMbecomes available to the research on the interpolation of wind speed.

wind speed, interpolation,This study investigates the theoretical background of the interpolation methods that regardsthe topographical effect on the climate data, such as Co-kriging, Artificial Neural Network and MKPRISM(Modified Korean Parameter-elevation Regressions on Independent Slopes Model). Prior toapplying the MK-PRISM to the interpolation of wind speed, this study has improved the model tobe closer to the fundamental concept of the PRISM and verified it‘s validity. Since each method hasindividual advantages and disadvantages, there will be a need for comparative studies in order to select aninterpolation method that is suitable for the topography of Korea. This study has added a weighted valuethat considers the existence of clusters at the known point, and has supplemented the digital elevationmodels and aspects distribution of multiple scales for application. In addition, this study has allowedthe consideration of sharp changes between the known point and unknown point when calculating thetopographic facet weighting. The supplement model was verified through the interpolation of rainfall inJeju Island. The coefficient of determination and KGE(Kling and Gupta Efficiency) of the model displayedthe results of 0.86 and 0.87, respectively for August 2010 monthly precipitation in Jeju Island, and themodel was accordingly verified. This study is able to provide the necessary information to the researcherswho wish to interpolate the observation data of wind speed. Furthermore, the supplement MK-PRISMbecomes available to the research on the interpolation of wind speed. MK-PRISM, Co-kriging, Artificial Neural Network

wind speed, interpolation, MK-PRISM, Co-kriging, Artificial Neural Network